Relationship networks having link quality metrics with inference and concomitant digital value exchange

ABSTRACT

In a digital social relationship network, a social network server computer stores a digital social network representation corresponding to a graph having nodes representing individuals or groups and links representing actual social relationships between the individuals or groups. The server computer obtains relationship-dependent information corresponding to a plurality of links of the graph, and embeds the relationship-dependent information in the digital social network representation stored in the social network server computer. The server computer interactively presents to a user of a client computer connected to the social network server computer a social network of the individuals or groups and the social relationships between the individuals or groups. The social network server computer receives input from the user of the client computer selecting at least one of the social relationships between individuals or groups other than the user, and presents to the user of the client computer a social relationship profile comprising the relationship-dependent information corresponding to the social relationship selected by the user of the client computer. The server computer facilitates exchange of digital value to the user of the client computer, or a group to which the user of the client computer belongs, based on the relationship-dependent information embedded in the digital social network representation stored in the social network server computer.

TECHNICAL FIELD

The field of the invention generally relates to computer-based socialrelationship networks, and more particularly to social relationshipnetworks employing embedded information characterizing specificrelationships between parties to the network, or social relationshipnetworks employing exchange of digital value.

BACKGROUND

Computer-based social networks such as FACEBOOK, GOOGLE+, PING, orLINKEDIN provide opportunities for individuals to maintain, nurture, anddevelop relationships with friends or business contacts. These networkstypically enable their participants to view profiles of otherparticipants, and to link with other participants with whom apre-existing actual relationship exists or with whom an actual social orbusiness relationship is desired. Typically, once linked togetherthrough a computer-based social network, participants can exchangecommunications, photographs, or other media content, and can view theidentities of persons with whom the other participant has relationshipsthrough the social relationship network. The other participant mighthave strong relationships with some of these persons, and might barelyknow others.

It is known, in contexts other than computer-based social networks, toprovide, to a human player of a computer-based game, informationcharacterizing the attitudes of other non-human “players” toward thehuman player. The information can include a linguistic description ofthe other player's attitude toward the human player (friendly, annoyed,etc.) and a set of reasons underlying the other player's attitude. Thehuman player can also look up a simple description of the relationshipsbetween the other players.

In the modern context of computer-based social networks, however, it istypically assumed that the participants are themselves aware of thenature of their own relationships with other actual human participants.

In a social network such as FACEBOOK, a profile of a particular personcan identify another person to whom the person is married or engaged,etc., along with an anniversary date.

The calculation of link quality metrics in social networks is known inthe art. For example, Eberle, U.S. patent application Ser. No.11/743,866 (Publication No. 2008/0120411) discloses a method forproducing a quantitative “scores” of the quality of relationshipsbetween the participants of a social network, based on variety offactors.

SUMMARY

In one general aspect, the invention provides a digital socialrelationship network and a method of operating such a network, in whicha social network server computer stores a digital social networkrepresentation corresponding to a graph having nodes representingindividuals or groups and links representing actual social relationshipsbetween the individuals or groups. The server computer obtainsrelationship-dependent information corresponding to a plurality of linksof the graph, and embeds the relationship-dependent information in thedigital social network representation stored in the social networkserver computer. The server computer interactively presents to a user ofa client computer connected to the social network server computer asocial network of the individuals or groups and the social relationshipsbetween the individuals or groups. The social network server computerreceives input from the user of the client computer selecting at leastone of the social relationships between individuals or groups other thanthe user, and presents to the user of the client computer a socialrelationship profile comprising the relationship-dependent informationcorresponding to the social relationship selected by the user of theclient computer.

By interactively presenting the social relationship network to the usersuch that the user can select a social relationship and thereafterreceive a social relationship profile, the invention greatly facilitatesthe ease with which a person can develop business contacts or engage inpersonal networking, because the user is armed with importantinformation about how well different people in the computer-basednetwork are actually connected to each other, as well as details that gobeyond a quantitative score. For example, the relationship profile caninclude information about the shared interests or business dealings ofthe parties to a relationship, and if this information is available to aclient user then the client user can rely his or her relationship withthe party he or she knows as well as knowledge of the shared interest orbusiness dealings to build a relationship with the party he or she doesnot know, for business networking or other purposes.

The user's networking abilities can be enhanced, in certain embodimentsof the invention, by the network server computer using a shortest pathalgorithm to identify a pathway within the social network from the userto an individual selected by the user, or the sever computer using amaximum flow algorithm to determine multipath reachability of anindividual selected by the user, thereby assisting the user inidentifying networking strategies with respect to the individualselected by the user.

Certain embodiments of the invention provide techniques that can assista user in identifying relationships to examine more closely. Forexample, at least some of the relationship-dependent information, suchas relationship strength information in quantitative or non-quantitativeform, may be presented to the user before the user selects one of thesocial relationships and before the social relationship profile is inturn presented to the user. The step of interactively presenting thesocial network to the user can include generating a list ofrelationships in response to a query from the user, or generating a listof potential social relationship partners based on the embeddedrelationship-dependent information.

Furthermore, the relationship-based information provided by socialrelationship networks according to the invention can be used tofacilitate exchange of digital value to users based on therelationship-dependent information embedded in the digital socialnetwork representation stored in the social network server computer.Such digital value can be provided to users based on the strength oftheir relationships, or based on introductions they have made within thesocial relationship network, or based on their shared interests, forexample. The digital value can be, for example, digital or virtual cash,coupons, tokens, advertising, access to a multiplayer game or game valuewithin such a game, or content having digital rights management, which auser can share within the social relationship network.

Moreover, the relationship-dependent information can be automaticallyinferred within the social relationship network from a variety ofsources, including: monetary exchange transactional metrics, frequencyof digital exchange, biometric information, visual cues, parsedlinguistic descriptions of exchanges between individuals or groups,geo-location information, game or group participation metrics, gesturalor haptic input, location dependence in digital media or photographsbetween individuals or groups in a relationship with each other, orcorrelation of routes of travel shared between participants in arelationship with each other (the participants either travelling theroutes concurrently or with temporal offset).

In certain embodiments of the invention, the automated inference of therelationship-dependent information can be based on analysis of theneighborhood of the graph a particular link, such as by analyzing degreeof interconnectivity within the neighborhood, and privacy accesssettings can be determined based on this automated inference. In otherembodiments of the invention, the overall quality of interconnections ofa subset of the graph is assessed, either quantitatively ornon-quantitatively, and the server computer initiates exchange ofdigital value to a user based on the assessment of the overall qualityof interconnections of the subset of the graph. In other embodiments ofthe invention, the quality of interconnections within the social networkis assessed based on the embedded relationship-dependent information,and a subset of the graph having high-quality interconnections isidentified based on the step of assessing the quality ofinterconnections within the social network.

In certain embodiments, the relationship-dependent information can besocial relationship strength information obtained by receivinglike/dislike/rating information for social relationships, thelike/dislike/rating information being a like/dislike/rating of thesocial relationships themselves. In other embodiments, therelationship-dependent information can include a characterization of thepersonality of the social relationship, which is presented to a userwhen the user accesses a link to which the relationship-dependentinformation corresponds. In other embodiments, therelationship-dependent information can be presented to a user when theuser accesses a link to which the relationship-dependent informationcorresponds, in accordance with privacy access settings for the link setby individuals or groups in a social relationship to which the linkcorresponds.

The social relationship profile can be in the form of a web pagecorresponding to the social relationship selected by the user of theclient computer. The relationship-dependent information can includemusic, artwork, or written literature, which is presented to the userwhen the user accesses a link to which the relationship-dependentinformation corresponds.

The digital social network representation may be stored as the graph ofnodes representing individuals or groups, or as a line graph havingnodes representing the social relationships between the individuals orgroups. The relationship-dependent information embedded in the digitalsocial network representation may be stored in the social network servercomputer itself or elsewhere.

In another general aspect, the invention provides a digital socialrelationship network and a method of operating such a network, in whicha social network server computer stores a digital social networkrepresentation corresponding to a graph having nodes representingindividuals or groups and links representing actual social relationshipsbetween the individuals or groups. The links of the graph include linksrepresenting social relationships between a user of a client computerconnected to the network server computer, or a group to which the userof the client computer belongs, and other individuals or groups. Theserver computer obtains relationship-dependent information correspondingto a plurality of links of the graph, and embeds therelationship-dependent information in the digital social networkrepresentation stored in the social network server computer. The servercomputer facilitates exchange of digital value to the user of the clientcomputer, or a group to which the user of the client computer belongs,based on the relationship-dependent information embedded in the digitalsocial network representation stored in the social network servercomputer.

The details of various embodiments of the invention are set forth in theaccompanying drawings and the description below. Other features andadvantages of the invention will be apparent from the description, thedrawings, and the claims.

DESCRIPTION OF THE DRAWINGS

FIG. 1 is a system overview diagram of the components of a socialrelationship network in accordance with the invention.

FIG. 2 is a flowchart diagram of the steps for embeddingrelationship-dependent information into a social relationship networkrepresentation in accordance with the invention, accompanied by examplesof a social relationship graph and social relationship line graph.

FIG. 3 is a graphical diagram of the steps for inferring link qualitymetrics in accordance with the invention.

FIG. 4 is a diagram of a social network graph annotated with linkquality information in accordance with the invention.

FIG. 5 is a graphical diagram of the steps for digital value exchangebased on embedded relationship-dependent information within a socialrelationship network in accordance with the invention.

FIG. 6 is an image of a data query element of a relationship selectionbrowser page produced in connection with a social relationship networkin accordance with the invention.

FIG. 7 is an image of a results list element of a relationship selectionbrowser page produced in response to data query entered in the dataquery element of FIG. 6.

FIG. 8 is a diagram of a dynamic network display element of arelationship selection browser page produced in connection with a socialrelationship network in accordance with the invention.

FIG. 9 is a close-up image of a portion of the dynamic network displayelement of FIG. 8.

FIG. 10 is an image of a relationship profile browser page produced inconnection with a business social relationship network in accordancewith the invention.

FIG. 11 is an image of a relationship profile browser page produced inconnection with a personal social relationship network in accordancewith the invention.

FIG. 12 is a flowchart diagram of steps for performing the graphtheoretic analysis and preprocessing step shown in FIG. 3.

FIG. 13 is a flowchart diagram detailing the decision analysis formingthe digital exchange step shown in FIG. 5.

DETAILED DESCRIPTION

The inventor has developed a social relationship network thatautomatically infers link quality metrics between its participants andalso employs digital value exchange based on these metrics.

The social relationship network employs the mathematical tools of graphtheory to characterize the dynamics of individuals and groups. A graph Gis defined as an ordered pair (V, E) of a set V of vertices or nodes anda set E of edges, which are two-element subsets of V. A line graph of anundirected graph G is defined as another graph L(G) that represents theadjacencies between edges of G. That is to say, any two vertices of L(G)are adjacent if and only if their corresponding edges in G share acommon endpoint (“are adjacent”).

With reference to FIG. 1, there is shown an overview diagram of thesocial relationship network. A relationship network server 400 includesone or more computer processors and data storage devices. Relationshipnetwork server 400 is programmed, in accordance with the detailsdescribed below, to store a representation of the digital socialnetwork. The representation of the digital social network corresponds toa graph having nodes representing individuals or groups and linksrepresenting actual social relationships between the individuals orgroups. Relationship network server 400 is also programmed, inaccordance with the details described below, to obtainrelationship-dependent information for the links of the graph and toembed this information in the digital social network representation. Therelationship network server 400 need not be centralized, but can bedistributed on many systems as is known to those skilled in the art.

The digital social network representation stored by relationship networkserver 400 is a data structure that captures the information containedin the nodes and links of the graph, as well as the embeddedrelationship-dependent information mentioned above. The social networkis modeled in the stored digital social network representation as agraph between participants. Nodes represent participants such asindividuals, organizations, or groups, and the links or edges betweenthe nodes represent the connections between the participants. Therelationship-dependent information embedded in the digital socialnetwork representation includes numerical values for the links of thegraph, and thus, the graph is a weighted graph. Social networkundirected or directed graphs (as data structure and analysis tool) areknown to those skilled in the art.

In alternative embodiments of the invention, the social network ismodeled in the stored digital social network representation as a socialnetwork based on relationships instead of individuals. The model isestablished by computing the line graph corresponding to theconventional social network graph. In this model, a relationship is anode in the corresponding line graph. If relationships in a particularsocial network involve more than two persons per relationship, then the“line graph” model (which applies only to two-party relationships) isextended accordingly to more than the two parties.

Relationship network server 400 is connected through an IP network toclient computer systems 401, which include personal computers andsmaller computer devices such as mobile tablet computers, smartphones,etc. As is described in more detail below, relationship network server400 is programmed to interactively present to client computer systems401 the social network of individuals or groups and the socialrelationships between the individuals or groups, to receive input fromclient computer systems 401 selecting social relationships betweenindividuals or groups, and, in response, to present social relationshipprofiles to client computer subsystems 401. The social relationshipprofiles include the above-mentioned relationship-dependent information.

With reference to FIG. 2, there is shown a flowchart diagram of thesteps for embedding relationship-dependent information into a socialrelationship network representation, accompanied by examples of a socialrelationship graph 1001 and a social relationship line graph 1003. Thenumeric labels in social relationship graph 1001 are placeholders orindices into data or information that the relationship network servercauses to be stored with respect to the individual nodes of the graph(corresponding to participants in the social network, which may beindividuals, companies, and other affinity organizations). In step 1002of the flowchart, this standard digital social network representation isstored, including the abovementioned data or information with respect tothe various individual nodes, stored at the node level (i.e. nodes 1, 2,3 and 4 in social relationship graph 1001). In step 1004 (which isoptional), this stored digital social network representation isconverted to a line graph, such as social relationship line graph 1003,the nodes in line graph 1003 corresponding to edges from graph 1001 asshown in list 1005. The nodes in line graph 1003 are defined by sets ofcardinality two, since the links in graph 1001 are undirected.Nevertheless, in a case in which the links in graph 1001 are directed(links corresponding to unidirectional relationships such as linksrepresenting fans of a given node, for example), the nodes in line graph1003 are defined by ordered pairs rather than sets of cardinality two,as is known to those skilled in the art. If optional step 1004 is notperformed, the digital social network representation data is left in itsoriginal form from step 1002.

In step 1010, relationship-dependent appurtenant information(relationship data) is stored with the edges in social relationshipgraph 1001 or, if optional step 1004 is performed, with the nodes insocial relationship line graph 1003, depending on whether optional step1004 is performed. A conventional social network corresponding to socialrelationship graph 1001 contains node-specific data and operates on thatdata as is known to those skilled in the art. A network corresponding tosocial relationship line graph 1003 operates in analogous terms onindividual relationships in the network, with the functionality ofexisting social network facilitation tools (such as FACEBOOK, LINKEDIN,PING, and GOOGLE+). The system culminates in step 1020 with a digitalrepresentation of the social relationship network, embedded with therelationship-dependent information.

The quality of interactions among participants of the social network ishighly subjective and interpersonally defined. Nevertheless, therelationship network server employs mechanisms for inferring the qualityof the connection between nodes in a meaningful quantitative orlinguistic way, thereby providing a tool useful for follow-on exchangeof digital value based on the inferred quality as well as for otherpurposes. With reference to FIG. 3, there is shown a flowchartillustrating the inference process for the link quality metric performedby the relationship network server. A variety of data sourcesparticipate in the determination of link quality metrics. Accordingly,in various embodiments of the invention, this inference is based uponmetrics associated with time-dependent and stochastic transactional data2000 such as virtual or real monetary exchanges between the networkparticipants (including frequency of digital exchanges, which can beobtained by linking to PAYPAL information and the like), biometricinformation 2010, visual cues 2020 such as facial expressions and microexpressions (which can be obtained from video calls and chats) orproximity of the participants to each other in photographs and othervisual media stored in the participants' own databases (based on facialrecognition technology), linguistic parsing and analysis of exchangesbetween the participants 2030, geo-location information 2040 (such asproximity of location information between the network participants andcommon time-dependent routes followed by the network participants),participation in group or individual games (or virtual worlds) 2050,gestural or haptic input 2060 (such as typing rhythm), direct user orgroup input (such as “like,” “dislike” or other ratings of therelationship itself, provided by the participants in the relationshipthemselves or by other participants of the network), and metricsassociated with analysis of the graph data structure 1990, such asanalysis of the degree of interconnectivity of the network in theneighborhood of the link under analysis (discussed below in connectionwith FIG. 12).

Thus, a very broad range of inputs is collected and calculated for linkquality establishment.

More specifically, data 1990 pertaining to the graph structure isprocessed through graph theoretical analysis and preprocessing step 3990(detailed in FIG. 12 in the case of analyzing interconnectivity of aneighborhood) to obtain a value for the link quality for that inputstream. In calculating the link quality value, for example, aggregateweightings of cliques can be used or k-vertex connectivity in the socialnetwork can be used. In addition, time-dependent and stochastictransaction data 2000 (data that is continually being updated by datasources) are analyzed. These data may pertain to the graph structureitself (i.e. frequency of edge deletes and adds between nodes) or can beextrinsic virtual or real financial transactions. Stochastic analysisdetermines the degree to which the transactional data between two nodesdeviates from the universe of a known set of typical transactions. Thedegree of typicality is captured in a value that is used to form anintermediate transactional data metric 4000. If the transactional data2000 pertains to financial transactions, than a low transactional datametric would correspond to low economic activity between the two nodes.If one of the nodes represents a charitable organization, then a historyof making monetary donations to the charitable organization wouldwarrant a high transactional data metric for the relationship betweenthe donor and the charitable organization.

Concurrent collection of biometric data 2010 by biometric means for theparticipants in the social network yields a degree of correlation forinput to the establishment of a biometric link quality metric 4010. Anexample of biometric time series is galvanic skin response (GSR),electrodermal response (EDR), psychogalvanic reflex (PGR), skinconductance response (SCR) or skin conductance level (SCL). Other typesof biometric collection of response can be conducted as known to thoseskilled in the art such as electroencephalogram, magnetoencephalogram,electrocardiogram, electromyogram, and heart rate variability (forexample, Tan et al., U.S. Patent Application Publication 2007/0185697describes the use of electroencephalograph signals for taskclassification and activity recognition). The time series of theresponse is correlated to determine similarity of responses to likestimuli to form the biometric link quality metric 4010. Also included inthe input data sets are visual cues 2020, adapted from image processing.Proximity in photos, decoded expressions (with participants beingidentified by facial recognition technology), decoded time-dependentexpressions (i.e. micro expressions from visual chats), and decoded bodylanguage form the basis of the visual cue metric 4020. Parsed linguisticdescriptions of exchanges 2030 are data pertaining to email messagesexchanged between participants, audio files, multimedia files, documentsin a shared workflow, or comments shared in a social network data field.The linguistic data contains semantic content that is parsed andanalyzed for keywords, shared world view and ideas. The degree to whichparticipants share semantic content is summarized in a metric pertainingto this area of exchange 4030. Geo-location information 2040 can be timedependent as well and plays a role in the establishment of the degree towhich parties share common time dependent routes and locations, toestablish a metric 4040 based on time-stamped geo-location information.Recorded group participation data 2050 includes time-dependent join andleave operations, including participation in games and group activitiesas well. Metric 4050 is based on group activities and takes into accountmutual group membership. Moreover, overall link quality metrics for agroup are used in aggregate (in a manner analogous to the steps shown inFIG. 12, discussed below) to obtain a weighting for the degree ofinterconnectedness of that group. This information is used to obtain thegroup weighting 4050 as it pertains to individual link quality. Gesturaland haptic feedback 2060, digitized from virtual games or even visualcues as well, is captured as it pertains to the relationship linkquality in a metric 4060.

User input data 2070 is obtained from a “like” button, or an inputmechanism that accepts a gradation of possible values for degree of“like/dislike” values, or a rating system that allows members of a givencommunity (nodes in the social network graph) to rate directly a givenrelationship between two parties or nodes in the relationship graph.

User input data 2070 can also be obtained from the frequency of commentsfrom third parties about the relationship on a relationship profile page(described below in connection with FIGS. 7 and 8). Extrinsic andintrinsic ratings of link quality (by the participants to therelationship and by third parties, respectively) are captured in thedata 2070, which is used to obtain metric 4070. Metric 4070 can includea multi-dimensional assessment of the relationship similar to a MyersBriggs assessment of personalities of individuals (characterizing therelationship as being an introverted or extroverted relationship, forexample). This kind of information can be very useful to people who wishto develop networking strategies using the social relationship network,or to advertisers.

Ellipses are shown in FIG. 2 to indicate that other input sources ofdata can be used. For example, the relationship network server canconduct searches through search engines for source material or datadirectly related to the relationship (such as articles in which aperson's name occurs in “proximity” to another person's name), and thissource of data could be used as a measure of link quality. Privacysettings set by the parties to the relationship, and psychologicalfactors as indicated by online behavior can also be used to infer thequality of the link. Link quality can also be inferred from data such asrate of change of links by a particular network participant, including,adds, deletes, and modifications of privacy restriction.

All metrics are passed to weighting function 5000, which calculates anoverall link quality metric. The weighting function is any of a numberof classification technologies known to those skilled in the art. Forexample, a support vector machine (SVM) or a Bayes Classifier can beused where the link metric is a discrete set of values. Bothprobabilistic classifiers and non-probabilistic classifiers can be used.In certain embodiments of the invention, the metric, as stated before,is nonnumeric and linguistic in lieu of a quantitative measure.

The steps shown in FIG. 3 are iterated for all links in the graph, ornodes in the line graph. At the end of this process a link qualitymetric has been established at weighting function 5000 for each graphlink or line graph node. After processing at weighting function 5000 forall links in the network, a link-quality annotated social network graphis obtained. The feedback of link quality now is available for thesocial graph.

With Reference to FIG. 4, there is shown a simple example of alink-quality annotated graph 7010. Graph 7010 is undirected; however, itis a simple matter to show links as a directed graph, as is discussedabove. An example rating system 7000 for the social network graphincludes numeric values, but the actual annotations can be non-numericvalues that give insight into the link quality of a connection. Hencethe weightings associated with graph edges and nodes can be linguisticin nature and not just numbers and factors.

With reference to FIG. 12, there is shown a flowchart diagram of thesteps for performing graph theoretic analysis and preprocessing for asubset of the graph, which steps collectively form step 3990 of FIG. 3with respect to a particular link under analysis. In the process of FIG.12, an overall quality of the interconnections of a graph is derived fora subset of the graph (such as a neighborhood of the particular linkunder analysis in FIG. 3), based on the individual relationships andtheir quality within the subset of the graph. First, a subgraph of therelationship network is identified and searched based on a query of linkor node data (step 300). For the particular link under analysis, thelink and network quality metrics for the identified subgraph areobtained (step 301), and these quality metrics are averaged or processedthrough a graph algorithm to provide a single metric for the particularlink under analysis (step 302), which is forwarded to the overallweighting function (block 5000 in FIG. 3) that combines this metric withall other metrics for the particular link under analysis. A feedbackloop (step 303) causes steps 300-302 to be repeated for other links tobe analyzed.

Aggregating link quality information produces a picture of the degree ofconnectedness beyond traditional graph theoreticalk-vertex-connectedness measures (k being the smallest number of verticesthat can be deleted from a subset of the graph to cause the subset ofthe graph to become disconnected). Weightings of connectedness due tonumber of unique elements of data shared, for example, provide a metricthat can determine to a greater extent the degree to which groups ofnodes (and the people they represent) are connected to each other andshare common relationship data. Clustering coefficients (measures of thedegree to which the nodes in the subset are clustered together) can beused as part of the assessment of aggregate link quality for the subset.

The information provided by the overall metric for the subset of thegraph benefits targeted marketing and advertising to the group definedby the subset of the graph, and benefits other types of digital valueexchange. In certain embodiments of the invention the overall quality ofthe subset is relied upon to facilitate or deny access to furtherdigital value exchange for that subset.

Identification of subsets can be by means of standard database querymechanisms (such as keywords associated with nodes or links, or queriesconcerning a company for whom all participants of the subset work).Standard logic applies for selection of the subsets. One means of subsetidentification can be connectivity and link quality (in other words,select a subset with good link quality).

The link quality metric produced by the inference processes of FIGS. 3and 9 is quantitative in some embodiments of the invention, and in otherembodiments the metric contains non-numeric, linguistic, cultural, ormultimedia descriptions in lieu of or in addition to quantitativescores. Moreover, a link of the graph can also define items of interestthat transcend a mere Venn-diagram intersection of shared interests. Forexample, as will be discussed in more detail below, therelationship-dependent information embedded in the digital socialrelationship network representation can include “likes” and “dislikes”for a relationship itself, provided by the participants in therelationship themselves or by other participants of the social network.Other embedded definitional descriptors of the relationship itself caninclude likes/dislikes (for activities or forms of entertainment, etc.).Other forms of embedded definitional descriptors include actual musicfiles, visual artwork files, poetry, multimedia content, and culturalcontent, attached to a graph link that defines a relationship in thesocial network, or attached to a corresponding node in the line graphcorresponding to the graph. Thus, the social network is defined by therelationships and not just by the individual participants. Furthermore,the information described above can be input to the establishment of alink or relationship quality metric in the process described above inconnection with FIG. 3. Those metrics, whether in aggregate for asubgraph or individually on a link, can be used to conditionally allowdigital exchange in the network as is described below.

The relationship network server interacts with the client computersystems to enable privacy settings to be set by the participants to arelationship, and in certain embodiments of the invention these privacysettings can be inferred from the link quality metrics. Thus, theprivacy setting can be set so that the shared content described above isprivate to the participants to the relationship and not shared (in anextreme case, the participants would have to log in together to view theshared content), or so that it can be shared with others (includingspecific privacy settings for specific categories of relationships suchas social friends, business contacts, etc.). Furthermore, in certainembodiments of the invention this shared content or relationshipappurtenant data is analyzed as part of the process of inferring linkquality metrics shown in FIG. 3.

As was mentioned above, in certain embodiments of the invention thesocial network is modeled in the stored digital social networkrepresentation as a social network based on relationships instead ofindividuals, by computing the line graph corresponding to theconventional social network graph, a relationship being a node in thecorresponding line graph. In these embodiments, the quality metricsdiscussed above can be established either for nodes (relationships) inthe line graph, or in a somewhat analogous manner for the links in theline graph (in which case the quality metric is for the link betweenrelationships in the line graph). In this document when graph qualityand link quality metrics, and follow-on digital value exchangeactivities are discussed, these discussions shall apply equally to thissocial network of relationships context as well.

In operation of the social relationship network, the relationshipnetwork server 400 of FIG. 1 interacts with client computer systems 401to receive queries from users of the client computers and, in responsethereto, to present “results” to the client computers in the form ofsuggested relationship links or in the form of a dynamic, interactivedisplay of a portion of the social relationship network. In particular,with reference to FIG. 6, there is shown an example of a data queryelement 620 of a relationship selection browser page that appears at oneof the client computer systems. In this example, the user has a strongrelationship with Alice and wishes to identify persons he or she canreach through his relationship with Alice. The user selects Alice in aselection box and chooses, from another selection box, to search forrelationships in which “one party knows Alice,” “both parties knowAlice,” or “both parties don't know Alice.” In another selection box,the user chooses to search for relationships in which the parties to therelationship have a “high quality link,” “medium quality link,” “lowquality link,” or “no link.” In other embodiments of the invention,other types of pre-selection are used in place of what is shown in FIG.6: For example, the client user can search an individual, to find outabout that person's relationships, or, the client user can start at theuser and expand outward, or the client user could enter a query for typeof individual, or a certain type of relationship the client user isinterested in.

In one embodiment of the invention, the query entered in connection withFIG. 6 produces a results list as shown in FIG. 7, transmitted from therelationship network server to the client computer system. Each result621-622 represents a relationship link between participants in thesocial relationship network. Differences in coloring or shading of therelationship links in the results list can represent different qualitymetrics for the respective links. The links in the results list are“clickable.” such that a user can select a link and receive in responsefrom the relationship network server a profile page for the relationship(discussed below in connection with FIGS. 10 and 11). The images of theparticipants in the results list are also “clickable,” such that theuser can also receive traditional profile pages corresponding toindividual participants of the network.

In another embodiment of the invention, the query entered in connectionwith FIG. 6 produces a dynamic network display 601 as shown in FIG. 8,transmitted from the relationship network server to the client computersystem. The dynamic network display shows nodes representingparticipants in the network, and relationship links identical to thoseshown in FIG. 7. Thus, only relationship links responsive to the queryare shown. This dynamic display is “clickable,” such that by selecting alink a user receives a profile page for the relationship in response.The nodes are also “clickable” such that the user can also receivetraditional profile pages corresponding to the nodes. Vertical andhorizontal scroll bars, zoom features, and re-centering features allowthe user to explore portions of the network beyond the portion initiallyshown to the user. The client user can explore the network throughdynamic network display in a manner similar to a visual thesaurus. Onlyrelationship links exceeding a client-selectable quality metric (whichmay pop up on the browser page as a query) are included in dynamicnetwork display 601.

In other embodiments of the invention, the dynamic network display ofFIG. 8 is presented to the user of the client computer system withoutthe user first entering a query to search for relationships. Rather, thedynamic network display is either centered around a node representingthe user, or centered around a node representing a specific participantselected by the user. The user explores the dynamic network displayusing scroll bars and zoom features.

With reference to FIG. 9, there is shown a close-up image of a portionof the dynamic network display element of FIG. 8. Differences incoloring or shading of relationship links 605 and 606 representdifferent quality metrics for the links between participants 612 and 610and between participants 612 and 611 respectively.

With reference to FIG. 10, there is shown a relationship profile browserpage for a business relationship, either in the context of an entirelybusiness-based social relationship network, or a more general socialrelationship network that includes different categories of relationships(business, personal, etc.) The relationship server computer transmitsthe relationship profile to the client computer system when a clientuser selects, by clicking, one of the relationship links in one of FIGS.7-9. Participant identifiers 550 and 551 identify the parties to therelationship being profiled. Dynamic network display 552, which isanalogous to dynamic network display 601 in FIG. 8, displays a portionof the social relationship network in the neighborhood of therelationship link that corresponds to the profile, and includes“clickable” nodes and links. Only relationship links exceeding aclient-selectable quality metric are included in dynamic network display552. Quality of relationship display 553 provides an indication of thequality of the profiled link, either automatically inferred according tothe method discussed above, or based on direct input from participantsin the network.

Included in the profile page are: individual postings 554 and 555 by theparticipants to the relationship; presentations (such as slide shows)news items, and transactional content (such as PAYPAL or EBAYtransactions) 556; third-party postings 557; and joint postings by theparticipants 558 (as a tandem blog in which individuals and groups canmake joint postings). The participants to the relationship and thirdparties can enter the postings directly into the relationship profile.The participant and third-party postings can also be auto-generated:Unless messages are flagged as private, they are automatically posted onthe relationship page. This auto-generation can be performed by aplug-in app applied to a known social relationship network such asFACEBOOK. The plug-in app could have conventional FACEBOOK privacysettings, but could also create privacy settings for the relationshipprofile page too (as to who may view it, what kind of content can begenerated automatically, and what kind of content would not be allowedon the profile page, etc.).

With reference to FIG. 11, there is shown a relationship profile browserpage for a personal relationship, which is transmitted to the clientcomputer in the same manner as the business relationship profile of FIG.10 and which functions in the same manner. The personal relationshipprofile includes participant identifiers 150 and 151, dynamic networkdisplay 152, quality of relationship display 153, individual postings154 and 155 by the participants to the relationship; multimedia content,news items, and music files 156; third-party postings 157; and jointpostings by the participants 158; all of which function in a manneranalogous to their counterparts in the business relationship profile ofFIG. 10.

One novel use of the link quality information is to conditionallyestablish follow-on digital exchange inclusive of digital cash andadvertising based on the link quality information. Thus, in certainembodiments of the invention, calculations of graph theoretical linkquality metrics are followed by concomitant and conditional digitalvalue exchange in the network. For example, digital value exchange canbe digital or virtual cash, advertising, enhanced advertisingwatermarked with digital cash as described in U.S. patent applicationSer. No. 11/898,887, tokens, coupons, PAYPAL payments, access to amultiplayer game, game value within such a game, enhanced social statuswithin such a game, music or media content with or without digitalrights management, auction credits, EBAY payments, promotions, banktransfers, differential pricing, salary bonus structures, or otherexchanged values. In certain embodiments of the invention, participantsof the network are remunerated directly for having high quality linksand for making successful introductions through the network to othernetwork participants (perhaps at a charge). This remuneration can beanalogized in one instantiation to sharing of profits of INMAIL forLINKEDIN. Because the measurable quality of relationship links becomes avaluable commodity, participants in the social relationship network canbe remunerated by corporations for whom they work for producing goodrelationship links (especially for important clients of the corporation)or for producing a subset neighborhood of the social relationshipnetwork having good aggregate connectivity (as in the case of businessmanagers being rewarded for building good “teams”). Also, participantsof the social relationship network can be remunerated by third partiesfor providing introductions through the network.

With reference to FIG. 5, there is shown a diagram of the steps fordigital value exchange. In particular, the link quality or specificcontent of the relationship data, from link quality annotated graph7020, is used as the basis for targeting advertising 7030, digital cashor coupons 7040, shared content with digital rights management 7050 suchas music and multimedia, and other forms of digital value exchange suchas music and multimedia without digital rights management. In certainembodiments, advertising 7030 is targeted using the criteria that therelationship between two parties meets certain thresholds of quality.

Specific examples of digital exchange and the like include: discountedblocks of tickets to a concert; pairs of tickets or coupons inconnection with a dating network; “calls for action” in a politicalactivism network. In each of these examples the exchange is dependentnot only on the degree of connectedness but also on the specific natureof the shared interests (such as shared interest in a specific musicgroup or shared interest in a political cause).

In other embodiments, digital cash, coupons, or virtual currency 7040 issent to groups and individuals that share strong relationships (highlink quality metrics). Shared content 7050 such as games andapplications are provided to network participants, in certainembodiments, subject to the network, a subset of the network, orindividual links in the network having sufficient link quality to meritsharing of the information. The digital rights management of sharedcontent 7050 can be relaxed to allow location-dependent, time-dependent,or relationship-dependent use of the shared content. Thus, the digitalvalue exchange process enables conditional sharing of content based onthe link quality metric. For example, songs and paid content are sharedcontingent upon a high aggregate link quality of a subset of the networkor contingent upon high individual link quality, and, in certainembodiments, also contingent upon shared interest in a particular kindof music. In step 7100, the digital exchange items are conveyed to theparticipants in the network dependent upon the link quality metrics, anddependent upon shared interests of the parties to relationships. Withreference to FIG. 13, there is shown a flowchart diagram of the stepsthat comprise step 7100 of FIG. 5. In particular, transactional datasources and link quality graph structures 950 are analyzed in step 960based on decision logic for conveying digital value. This decisionlogic, in various embodiments of the invention, is based on thresholds,database queries, set logic, graph reachability, shortest pathweightings, or weighted link graph neighborhood analysis. Based on theanalysis of step 960, digital value is conveyed (step 970) or notconveyed (step 980).

In certain embodiments, the digital exchange facilitates e-commercetransactions between participants in the social network, yieldingcommercial exchange between members of the social network andrelationships in the network. The quality metrics can be used to enhancemarketing of products and advertising (with respect, for example, tofriendship-dependent products such as multi-player games that depend onthe relationships). Quality metrics are also used in virtual economy orgames to earn items of value (for example, the strength of a coalitioncould be adjudicated as a factor in winning a game against another teamin competition for monetary rewards). The overall quality of the networkmetrics helps marketers to target their ads. Graph analysis of a groupof people can indicate health of the group or affiliation, throughdetermining the degree of interconnectedness of the group. Qualitymetrics can also be used to determine access control and privacysettings of information on the social network: High-quality subnetworkscould have more sharing of information than subnetworks that do not havehigh-quality metrics. Similarly, relationships that have low qualitymetrics presumably would be protected by privacy access settings toavoid broadcasting the fact that a particular relationship is poor.These privacy access settings can be set by individual users, and alsoautomatically by the relationship network server. For example, circlesof privacy and access can be determined automatically or with some userinput through graph connectedness and link quality metrics.

The link quality metrics provided by the invention are also useful formany other purposes other than forming the basis for exchange of digitalvalue. For example, the information in the link quality metrics can beused as the basis for automated suggesting of “friends” to a client userof the social relationship network, based on the quality of the linksbetween the client user and other participants and the quality of thelinks between the other participants and the suggested “friends.” Incertain embodiments, rather than suggesting “friends” to the user foracceptance of decline, a list of “friends” can be auto-generated and theclient user can be automatically connected with the list of “friends,”based on the link quality metrics. Alternatively, such an automaticallygenerated list can be relied upon by the client user to selectparticipation in events and to determine whether another participant canread postings under privacy access settings established by the user.

The link quality metrics can alternatively be used to identify thedegree of multipath reachability of another network participantidentified by the client user, according to the known “maximum flowalgorithm” (if high link weighting numbers represent relatively goodlinks). Multipath reachability as calculated by maximum flow algorithmprovides a way of identifying the reachability of designated persons inthe social relationship network. While the maximum flow algorithm doesnot generate the best/shortest path to an individual as does thefollowing, it does provide a metric describing the reachability overmultiple paths simultaneously. This is useful in determining the degreeof simultaneous connection over multiple paths—“multipath reachability.”

The link quality metrics can also be used to identify the best path forreaching a specific individual through the social relationship networkidentified by the client user, according to the known “shortest pathalgorithm” (if low metric numbers represent good links). The ranking ofshortest paths can produce a priority list of the best ways to access anindividual in the social network.

In addition, the link quality metrics can be used to determine the“trustedness” of a subset of the social relationship network, whencombined with known graph analysis.

Members of the subset with stronger links can be rewarded, with monetaryrewards or incentives, for introducing other members of the subset toeach other, and for nurturing the link quality of weaker links withinthe subset, to improve the “trustedness” of the subset.

The inventor has described a network in which relationships haveincreased import over existing social network infrastructure. In oneimplementation, relationships are elevated to nodal status in the graphrepresentation and detailed content is stored and retrieved regardingthe relationships themselves. While several particular forms of theinvention have been illustrated and described, it will be apparent thatvarious modifications and combinations of the invention detailed in thetext and drawings can be made without departing from the spirit andscope of the invention. For example, the relationship network server andclient computer systems configuration of FIG. 1 could be replaced by adistributed peer-to-peer computation and storage system in which thetasks performed by the relationship network server of FIG. 1 are insteadperformed in a distributed manner by client computer systems themselvesthroughout the entire system. Also, references to specific flowchartsteps or specific browser pages and the elements thereof are also notintended to be limiting in any manner and other steps and elements couldbe substituted while remaining within the spirit and scope of theinvention. Accordingly, it is not intended that the invention belimited, except as by the appended claims.

What is claimed is:
 1. At least one non-transitory computer-readablemedium having computer-executable instructions embodied thereon, which,when executed by at least one processor, cause the at least oneprocessor to: store in a social network server computer a digital socialnetwork representation corresponding to a graph having nodesrepresenting individuals or groups and links representing actual socialrelationships between the individuals or groups, the links of the graphincluding links representing social relationships between a user of aclient computer connected to the network server computer, or a group towhich the user of the client computer belongs, and other individuals orgroups; obtain relationship-dependent information corresponding to aplurality of links of the graph; embed the relationship-dependentinformation in the digital social network representation stored in thesocial network server computer; receive, at the social network servercomputer, input from the user of the client computer selecting at leastone of the social relationships between individuals or groups; presentto the user of the client computer a user-viewable social relationshipprofile, specific to the social relationship selected by the user,comprising a user-viewable display of at least some of therelationship-dependent information corresponding to the socialrelationship selected by the user of the client computer; andfacilitate, using the network server computer, exchange of digital valueto the user of the client computer, or a group to which the user of theclient computer belongs, based on the relationship-dependent informationembedded in the digital social network representation stored in thesocial network server computer.
 2. At least one non-transitorycomputer-readable medium having computer-executable instructionsembodied thereon, which, when executed by at least one processor, causethe at least one processor to: store in a social network server computera digital social network representation corresponding to a graph havingnodes representing individuals or groups and links representing actualsocial relationships between the individuals or groups, the links of thegraph including links representing social relationships between a userof a client computer connected to the network server computer, or agroup to which the user of the client computer belongs, and otherindividuals or groups; obtain relationship-dependent informationcorresponding to a plurality of links of the graph, and performautomated inference of link quality metrics for the plurality of linksof the graph or based on analysis of neighborhoods of the graph; embedthe relationship-dependent information in the digital social networkrepresentation stored in the social network server computer; enabledigital social networking between the user of the client computer andthe other individuals or groups, including presenting to the user of theclient computer a user-viewable display of at least some of therelationship-dependent information corresponding to links of the graphrepresenting social relationships between the user of the clientcomputer and the other individuals or groups; and facilitate, using thenetwork server computer, exchange of digital value to the user of theclient computer, or a group to which the user of the client computerbelongs, based on a value of at least one of the automatically inferredlink quality metrics.
 3. At least one non-transitory computer-readablemedium having computer-executable instructions embodied thereon, which,when executed by at least one processor, cause the at least oneprocessor to: store in a social network server computer a digital socialnetwork representation corresponding to a graph having nodesrepresenting individuals or groups and links representing actual socialrelationships between the individuals or groups; obtainrelationship-dependent information corresponding to a plurality of linksof the graph; embed the relationship-dependent information in thedigital social network representation stored in the social networkserver computer; enable digital social networking between theindividuals or groups, including presenting to the individuals or groupsa user-viewable display of at least some of the relationship-dependentinformation corresponding to links of the graph representing socialrelationships between the individuals or groups; and facilitate, usingthe network server computer, exchange of digital value based on therelationship-dependent information embedded in the digital socialnetwork representation stored in the social network server computer. 4.A non-transitory computer-readable medium in accordance with claim 3wherein a link quality metric based upon which the exchange of digitalvalue is facilitated is social relationship strength.
 5. Anon-transitory computer-readable medium in accordance with claim 4wherein the social relationship strength is obtained by receivinglike/dislike/rating information for social relationships represented bythe plurality of links, the like/dislike/rating information being alike/dislike/rating of the social relationships themselves.
 6. Anon-transitory computer-readable medium in accordance with claim 3wherein a link quality metric based upon which the exchange of digitalvalue is facilitated measures introductions made by a user of a clientcomputer, or a group to which the user of the client computer belongs,within the social network.
 7. A non-transitory computer-readable mediumin accordance with claim 3 further comprising determining privacy accesssettings based on automated inference of link quality metrics.
 8. Anon-transitory computer-readable medium in accordance with claim 3wherein the exchange of digital value is based on link quality metricsthat measure a degree to which groups of the nodes, and the individualsor groups they represent, are connected to each other and share commonrelationship data.
 9. At least one non-transitory computer-readablemedium having computer-executable instructions embodied thereon, which,when executed by at least one processor, cause the at least oneprocessor to: store in a social network server computer a digital socialnetwork representation corresponding to a graph having nodesrepresenting individuals or groups and links representing actual socialrelationships between the individuals or groups; obtainrelationship-dependent information corresponding to a plurality of linksof the graph, and perform automated inference of link quality metricsfor the plurality of links of the graph or based on analysis ofneighborhoods of the graph; embed the relationship-dependent informationin the digital social network representation stored in the socialnetwork server computer; enable digital social networking between theindividuals or groups, including presenting to the individuals or groupsa user-viewable display of at least some of the relationship-dependentinformation corresponding to links of the graph representing socialrelationships between the individuals or groups; and facilitate, usingthe network server computer, exchange of digital value based on a valueof at least one of the automatically inferred link quality metrics. 10.A non-transitory computer-readable medium in accordance with claim 9wherein the link quality metric based upon which the exchange of digitalvalue is facilitated is social relationship strength.
 11. Anon-transitory computer-readable medium in accordance with claim 10wherein the social relationship strength is obtained by receivinglike/dislike/rating information for social relationships represented bythe plurality of links, the like/dislike/rating information being alike/dislike/rating of the social relationships themselves.
 12. Anon-transitory computer-readable medium in accordance with claim 9wherein the link quality metric based upon which the exchange of digitalvalue is facilitated measures introductions made by a user of a clientcomputer, or a group to which the user of the client computer belongs,within the social network.
 13. A non-transitory computer-readable mediumin accordance with claim 9 further comprising determining privacy accesssettings based on the automated inference of the link quality metrics.14. A non-transitory computer-readable medium in accordance with claim 9wherein the link quality metrics measure a degree to which groups of thenodes, and the individuals or groups they represent, are connected toeach other and share common relationship data.
 15. A non-transitorycomputer-readable medium in accordance with claim 9 wherein the analysisof neighborhoods of the graph comprises analysis of degree ofinterconnectivity within the neighborhoods.
 16. A non-transitorycomputer-readable medium in accordance with claim 9 wherein the linkquality metric based upon which the exchange of digital value isfacilitated is a metric of overall quality of interconnections for theneighborhood of the graph.
 17. A non-transitory computer-readable mediumin accordance with claim 9 wherein the automated inference of linkquality metrics is based on analysis of neighborhoods of the graph andthe neighborhoods of the graph are identified based on a query of linkor node data.
 18. At least one non-transitory computer-readable mediumhaving computer-executable instructions embodied thereon, which, whenexecuted by at least one processor, cause the at least one processor to:store in a social network server computer a digital social networkrepresentation corresponding to a graph having nodes representingindividuals or groups and links representing actual social relationshipsbetween the individuals or groups; obtain relationship-dependentinformation corresponding to a plurality of links of the graph, andperform automated inference of link quality metrics for the plurality oflinks of the graph or based on analysis of neighborhoods of the graph;embed the relationship-dependent information in the digital socialnetwork representation stored in the social network server computer; andfacilitate, using the network server computer, exchange of digital valuebased on a value of at least one of the automatically inferred linkquality metrics; wherein the automated inference of link quality metricsis based on analysis of neighborhoods of the graph and the neighborhoodsof the graph are identified based on connectivity, such that the step ofidentifying neighborhoods comprises selecting neighborhoods having goodlink quality within the neighborhoods.
 19. At least one non-transitorycomputer-readable medium having computer-executable instructionsembodied thereon, which, when executed by at least one processor, causethe at least one processor to: store in a social network server computera digital social network representation corresponding to a graph havingnodes representing individuals or groups and links representing actualsocial relationships between the individuals or groups; obtainrelationship-dependent information corresponding to a plurality of linksof the graph, and perform automated inference of link quality metricsfor the plurality of links of the graph or based on analysis ofneighborhoods of the graph; embed the relationship-dependent informationin the digital social network representation stored in the socialnetwork server computer; and facilitate, using the network servercomputer, exchange of digital value based on a value of at least one ofthe automatically inferred link quality metrics; wherein the automatedinference of link quality metrics is based on analysis of neighborhoodsof the graph and the analysis of neighborhoods of the graph comprisesanalysis of k-vertex connectivity.
 20. At least one non-transitorycomputer-readable medium having computer-executable instructionsembodied thereon, which, when executed by at least one processor, causethe at least one processor to: store in a social network server computera digital social network representation corresponding to a graph havingnodes representing individuals or groups and links representing actualsocial relationships between the individuals or groups; obtainrelationship-dependent information corresponding to a plurality of linksof the graph, and perform automated inference of link quality metricsfor the plurality of links of the graph or based on analysis ofneighborhoods of the graph; embed the relationship-dependent informationin the digital social network representation stored in the socialnetwork server computer; and facilitate, using the network servercomputer, exchange of digital value based on a value of at least one ofthe automatically inferred link quality metrics; wherein the automatedinference of link quality metrics is based on analysis of neighborhoodsof the graph and the analysis of the neighborhoods of the graphcomprises: obtaining quality metrics for the neighborhood; andaveraging, aggregating, or processing the quality metrics for theneighborhood through a graph algorithm to provide an overall qualitymetric for the neighborhood.